Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
This work addresses scalability issues for researchers and practitioners using HGNNs in applications like social and biological networks, though it is incremental as it builds on existing HGNN methods.
The paper tackles the scalability challenge of Hypergraph Neural Networks (HGNNs) by introducing an adaptive sampling strategy, which significantly reduces computational and memory demands while maintaining performance comparable to conventional HGNNs and baselines.
Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study, we introduce a new adaptive sampling strategy specifically designed for hypergraphs, which tackles their unique complexities in an efficient manner. We also present a Random Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron (MLP) module to improve the robustness and generalization capabilities of our approach. Thorough experiments with real-world datasets have proven the effectiveness of our method, markedly reducing computational and memory demands while maintaining performance levels akin to conventional HGNNs and other baseline models. This research paves the way for improving both the scalability and efficacy of HGNNs in extensive applications. We will also make our codebase publicly accessible.